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Research Article

Machine Learning for Nitrogen Content Estimation using SPAD Data in Shiranuhi (Citrus unshiu × C. sinensis)

‘부지화’의 SPAD 데이터로부터 질소함량을 추정하는 머신러닝 모델

Won Pyo Park1
,
Seong Heo2*
박 원표1
,
허 성2*
1Professor, Department of Plant Resources and Environment, Jeju National University, Jeju 63243, Korea
2Professor, Department of Horticulture, Kongju National University, Yesan 32439, Korea
1제주대학교 식물자원환경전공 교수
2공주대학교 원예학과 교수
*Corresponding Author
31 March 2022. pp. 88-94
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Abstract

In recent years, to investigate the nitrogen content in plant leaves, the use of non-destructive and simple methods is preferred to that of destructive, time-consuming, and expensive methods. In this study, several machine learning models (linear and polynomial regressions, stochastic gradient descent, artificial neural network, support vector machine, k-nearest neighbors, random forest, and gradient boosting) were applied to estimate the nitrogen content in leaves based on the linear relationship between the SPAD reading value and the nitrogen content in leaves of Shiranuhi (Citrus unshiu × C. sinensis). As the data of nitrogen content measured under the laboratory condition was insufficient, the data was increased using the bootstrapping method. Considering the model evaluation metrics, the gradient boosting model was selected as the most accurate model. The coefficient of determination of this model was the closest to 1 and the MSE, RMSE, and MAE all converged to 0, indicating that the error between the measured and predicted values was the smallest.

Keywords
Machine learning
Nitrogen content
Performance metrics
SPAD reading value
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Information
  • Publisher :Agriculture and Life Sciences Research Institute, Kangwon National University
  • Publisher(Ko) :강원대학교 농업생명과학대학 농업생명과학연구원
  • Journal Title :Journal of Agricultural, Life and Environmental Sciences
  • Journal Title(Ko) :농업생명환경연구
  • Volume : 34
  • No :1
  • Pages :88-94
  • Received Date : 2022-02-16
  • Revised Date : 2022-03-04
  • Accepted Date : 2022-03-15
  • DOI :https://doi.org/10.22698/jales.20220009
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